SaaS isn’t disappearing, but its role is changing. Do you know when to buy, build, or enhance?

For years, enterprise technology decisions have been framed as a binary choice: build custom solutions or buy packaged software. With the emergence of AI, agentic engineering capabilities, and changing economics, that binary is shifting.

Custom solutions are becoming more viable, with lower build and maintenance costs, greater speed, and more flexibility as agentic capabilities reduce the effort required to develop and sustain them. At the same time, SaaS platforms continue to evolve, with vendors embedding AI and adapting their delivery and pricing models as capabilities expand and costs scale over time. Together, these shifts are expanding the range of options and require a more deliberate approach to determining where SaaS is the right answer and where custom solutions now make more sense.

Rather than replacing existing platforms outright, organizations are increasingly combining SaaS and custom solutions, retaining platforms where they provide scale and reliability, while extending, augmenting, or selectively replacing them where differentiation, control, or economics justify it. These decisions are rarely made at the enterprise level, but instead at the domain level, with different choices across the technology landscape.

As a result of these decisions, enterprise environments are becoming more distributed, with work spanning vendor and enterprise systems. This increases the importance of clear accountabilities, strong data foundations, and cross-functional governance across technology, data, and business teams.

As organizations revisit build vs. buy decisions, they’re asking more targeted questions aligned to business outcomes:

• Where does differentiation justify building or owning capabilities?
• Where should we retain, enhance, or replace existing platforms?
• How do we move fast without added risk?
• What capabilities are required to build, operate, and sustain solutions effectively?

Here, we explore key insights to help organizations answer these questions and make more confident, forward-looking technology decisions.

Four pathways for navigating the shift

Designing for tomorrow requires moving beyond today’s assumptions. Economic and technology shifts are constantly redefining what is possible. Deferring action or designing only for today’s reality can limit future flexibility, lock in costs, and reduce differentiation.

At the same time, decisions are becoming more nuanced and domain-specific, with no single correct answer. Organizations are navigating trade-offs between the scale, stability, and vendor support offered by SaaS, and the greater control, flexibility, and differentiation enabled by custom solutions.

We see four common pathways emerging:

1. Vendor-led evolution

What it means

Retain existing SaaS platforms, align with the vendor’s roadmap, and adopt embedded AI capabilities as they are released.

When it fits

Best suited for standardized, regulated, or non-differentiating capabilities where consistency and compliance are prioritized over customization.

Why it works

Provides predictability, auditability, and operational simplicity, leveraging vendor scale, embedded innovation, and established operating models to reduce complexity and sustainment efforts.

Potential trade-offs

• Limits control over long-term AI cost trajectory and increases dependency on the vendor.
• Reduces future flexibility if capabilities or economics evolve faster than the vendor roadmap.

2. Agentic overlay

What it means

Maintain SaaS as the system of record while investing in an agentic layer that enhances user experience, orchestration, and business logic.

When it fits

Ideal when differentiation is required without disrupting core platforms or replacing existing systems.

Why it works

Enables faster differentiation with greater flexibility and control, allowing organizations to layer bespoke capabilities and logic across existing systems without full re-platforming.

Potential trade-offs

• Requires strong architectural discipline and can introduce integration complexity.
• Demands close alignment with vendor roadmaps to avoid duplicative or redundant investments.

3. Re-platform

What it means

Replace existing SaaS capabilities with AI-native solutions, often built or heavily customized to meet specific needs.

When it fits

Appropriate when legacy systems or vendors constrain growth, agility, or innovation, particularly for high-cost, low-flexibility platforms with undifferentiated logic, and where executive risk appetite exists.

Why it works

Offers greater ownership, long-term flexibility, and the potential to optimize cost and value over time, enabling solutions to be designed around specific business needs.

Potential trade-offs

• Requires early commitment to architectural decisions.
• Increases executive accountability for delivery, outcomes, and long-term sustainment.

4. Greenfield AI

What it means

Develop entirely new, AI-native applications from the ground up, including custom solutions and agentic workflows designed for specific outcomes.

When it fits

Best for net-new value creation where no legacy constraints exist, and where there is both risk appetite and organizational discipline to sustain new solutions.

Why it works

Maximizes differentiation and design flexibility, enabling organizations to create entirely new value propositions and operating models unconstrained by legacy solutions.

Potential trade-offs

• Operates within less mature or standardized models.
• Success depends heavily on deliberate design, governance, and ongoing operational discipline.

How do you choose the right pathway?

Start by asking the questions that matter most. Selecting the right pathway isn’t a one-size-fits-all decision. Each capability requires a clear understanding of where differentiation matters, how AI will reshape it, and what constraints may limit future flexibility.

The following questions help define the right approach, capability by capability.

Strategic differentiation

• Is this capability a true source of competitive advantage? Or is it better standardized?
• What levels of assurance, traceability, and auditability are required?

Agentic leverage

• Can agents meaningfully enhance or replace this capability?
• How will enterprise and vendor agents interact across systems and processes?

Vendor trajectory

• Is the vendor credible, and is their roadmap aligned with long-term needs?
• How are they evolving in response to AI disruption?

Technical constraints

• Do current limitations restrict flexibility, integration, or control?
• Can those constraints be addressed? Or do they require a different approach?

Economic and regulatory exposure

• How will costs evolve over time as usage scales or dependencies increase?
• What regulatory requirements will shape this decision?

Organizational capability

• Can the organization build, operate, and sustain this capability effectively?
• Are the governance and FinOps discipline in place to manage usage, cost, and value realization?

Navigating your enterprise software choices

We help organizations make confident, outcome-driven choices by aligning pathway decisions to business priorities and orchestrating the right mix of SaaS, custom solutions, and AI—independent of any single platform. By connecting strategy, technology, and AI capabilities, we help you navigate complex trade-offs and realize the full value of your enterprise systems.

1. Make the right pathway decisions, aligned to your business priorities

Choosing between vendor-led, overlay, re-platform, or greenfield AI approaches means aligning each choice to your business goals, risk appetite, and areas of differentiation.

• Evaluate pathway options based on business value, not just technical fit.
• Understand trade-offs across cost, control, speed, and flexibility.
• Ensure decisions work together across the broader enterprise.

2. Build the foundation to enable AI at scale

Once the pathway is defined, the focus shifts to strengthening data, architecture, and integration across systems.

• Design architectures that connect SaaS platforms, custom solutions, and agentic layers.
• Enable seamless integration to unlock value across systems and workflows.

3. Sustain and evolve value over time

As more AI-enabled and custom solutions are introduced, ongoing ownership, optimization, and evolution become critical to long-term success.

• Establish operating models to support and manage solutions effectively.
• Continuously optimize performance, cost, and capability mix.
• Adapt as technologies, vendor capabilities, and business needs evolve.

Are you making the right trade-offs?

Tech-enabled transformation decisions are no longer binary choices between SaaS and custom solutions. They require organizations to orchestrate the right mix of vendor-led, agentic overlay, re-platform, and greenfield approaches based on where they want control, differentiation, and long-term value to reside.

The organizations that succeed start with a clear vision, align each decision to outcomes, and architect solutions that balance speed, control, cost, and differentiation. As these choices become more interconnected, the challenge is ensuring they work together across the enterprise.

We can help you bring those choices together with clarity, confidence, and purpose. Connect with our leaders today.  

Thank you to our co-authors for their insights and contributions that brought this piece to life:

Anthony Chan
Tim Christmann
Nihar Dalmia
Niraj Dalmia
Bruce Derraugh
Mary Sanagan
Joel So
Blaine Woodcock